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Clear prospective associations exist between grip strength and multiple indices of health and well-being, including frailty, morbidity, and mortality. The usual explanation is that low grip strength arises from loss of muscle function, indicative of a more general decline in physical health.1 However, the relationship that also exists between grip strength and preservation of cognitive capacity2, 3—when cognitive and muscle function are not linked,4 suggests that this account is incomplete. Beyond middle age, individual variations in grip strength may predominantly be a marker of brain health, rather than physical status.5 Application of grip force demands sophisticated neuromuscular coordination and control. Advances in brain imaging make clear that neural degeneration is an orderly and sequential process6 affecting networks that mediate functionally related processes such as cognition and motor control.7 From this perspective, the proximate cause of the relationship between grip strength and cognitive decline is weakening of the functional integrity of brain networks. The distal cause is the dysregulation of multiple physiological systems, which ultimately also becomes manifested in the associations of grip strength (and cognitive function) with frailty, morbidity, and mortality.

We provide estimates of health priorities during the COVID-19 pandemic based on web-surveys administered in seven developing countries in Africa, Asia, and Latin America in 2022. Using the best-worst scaling method, respondents ranked the importance of seven health problems, including COVID-19 (the others were alcohol and drugs, HIV/AIDS, malaria, TB, other respiratory diseases, and water-borne diseases). Respondents in most countries considered COVID-19 a serious problem but ranked other respiratory illness as more serious. Respondents’ rankings were generally consistent with relative disease prevalence when it can be reasonably well measured (i.e., malaria and TB). Differences in priorities across countries were generally larger than within-country differences. The importance respondents assigned to COVID-19 was associated with their knowledge of COVID-19. These results have implications for the allocation of health resources: policymakers may face resistance if their actions are viewed as focusing too much on COVID-19 while neglecting other, potentially serious health problems.

Objectives Cortisol is known to promote adipocyte differentiation and maturation, and prolonged exposure to excess cortisol contributes to the development of obesity and metabolic dysregulation. The intracellular 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) enzyme catalyzes the conversion of inactive cortisone to active cortisol. Recently, we demonstrated reduced brain 11β-HSD1 levels in vivo with increasing body mass index (BMI), using positron emission tomography (PET) radioligands [11C]- and [18F]AS2471907(1). PET imaging can simultaneously quantify uptake in multiple organs (e.g., liver/brain). [18F]AS2471907 has high uptake and specific binding in the brain and liver(2). Liver and brain 11β-HSD1 measured by PET imaging may help elucidate the roles of cortisol activation in the setting of metabolic dysregulation. Thus, we performed PET imaging studies to examine 11β-HSD1 levels in the liver and brain. Methods Nine individuals (5F/4M) with a range of BMIs (22.6-34.4 kg/m2) underwent a 90 min PET/CT acquisition with arterial plasma sampling after injection of [18F]AS2471907. Regions-of-interest (ROI) for the liver were manually drawn on a summed PET image (60-90 min). Seventeen brain ROIs were selected from the anatomical automatic labeling (AAL) template and applied to the dynamic PET images to generate time-activity-curves (TACs). Brain volume of distribution (VT, mL/cm3), the target tissue to plasma ratio of radioligand at equilibrium, was estimated for each ROI using the multilinear analysis-1 method with plasma input function. Mean whole-brain VT values were calculated by averaging all ROIs. Given the appearance of irreversible kinetics in the liver, Ki (min-1), the rate of irreversible tracer uptake, was calculated using the Patlak method. Results Qualitative brain and liver uptake was assessed by examining a summed PET image (SUV 60-90min). The parent fraction in plasma was 88±1% at 90 min, indicating negligible plasma radiolabeled-metabolites contributing the measured signal in the liver. Kinetic modeling estimates demonstrated decreasing whole brain VT with increasing BMI (R2=0.53), similar to our previously published study examining only brain in a larger cohort(1). Patlak methods provided good estimates of Ki. In an opposite manner from the brain, correlations seen in the liver using Ki were positively correlated with BMI (R2=0.57). Conclusions These preliminary studies suggest obesity is associated with increased 11β-HSD1 levels in the liver but decreased 11β-HSD1 levels in the brain. Clinically, liver specific 11β-HSD1 inhibitors may prove beneficial in treating metabolic sequalae of obesity such non-alcoholic fatty liver disease (NAFLD) by inhibiting activity of higher 11β-HSD1 levels. Further studies in NAFLD and obesity are necessary to determine appropriate populations that would benefit from 11β-HSD1 inhibitors and longitudinal in vivo PET imaging to help inform development of future therapeutics. References [1] Bini, et al, Molecular Imaging and Biology, 2020. [2] Bini, et al, IEEE Trans on Radiation and Plasma Medical Sciences, 2021 Presentation: Saturday, June 11, 2022 1:42 p.m. - 1:47 p.m., Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

During the spring of 2020 ChoiceFlows Inc. (Choiceflows) researchers document an approach for small and medium-sized businesses to make informed decisions about equitable and resilient reopening after COVID-19 pandemic shutdowns and restrictions. The client is 501(c)(3) chartered Restart Partners in Washington State and their client, the State of Washington Department of Commerce. After several revisions, an agreement is signed on December 30, 2020 to make the approach real with development and deployment in 2021 using Cares Act Grant money from the U.S. Federal Government. The result is Smart WA – and can be freely accessed online here: https://smartwa.us Smart WA is the result of unique primary research conducted by Choiceflows involving citizens and residents of Washington State, and many days of secondary research finding publicly available data sources and bringing them together in one place to provide both comprehensive and easier to use data to make informed decisions. The research behind Smart WA is the first study to comprehensively examine how specific COVID-19 transmission reduction actions influence planned visits to different types of businesses. Data on Smart WA is organized by Human Health, Economic Health, and Community Experience metrics – and displayed as composite scores. The entire online system is powered by Tanjo.ai machine learning, a Choiceflows business partner, and is updated from all data sources daily.The advent of the COVID pandemic disrupts a wide range of businesses that directly serve the public and causes a dramatic fall in visits to these establishments. Businesses face a wide range of options in how to respond ranging from the pre-pandemic status quo and doing nothing to prevent the spread of the virus to shutting down businesses due to a lack of customers.Recognizing this, Smart WA has a “what-if” game-like section that allows a business to model the choices that they can make for reopening, and what they can expect from customers based on real data from people in the state. The relevant question from a small business perspective is: If we take an action or actions that influence COVID-19 transmission and make it known to our customers, how will that influence those customers to visit? The research supporting this function was conducted in four waves of surveys designed and administered during 2021, making it one of the most comprehensive research programs of its type during COVID with snapshots of customer preferences being collected over several month intervals.We document the development of this COVID Choices research using the Choiceflows pioneered Volumetric Choice Experiment (VCE) method and design and in parallel the online system. This provides a platform for projecting how the insights and methods from this work can be used for other issues facing small and medium size businesses to aid in and speed decision making and choice. This includes and dashboards for policymakers and main street for allocating resources for commerce, including economic development, ongoing community health, and supporting quality of life indicators.

Purpose GluN2B containing N-methyl-D-aspartate receptors (NMDARs) play an essential role in neurotransmission and are a potential treatment target for multiple neurological and neurodegenerative diseases, including stroke, Alzheimer’s disease, and Parkinson’s disease. (R)-[18F]OF-Me-NB1 was reported to be more specific and selective than (S)-[18F]OF-Me-NB1 for the GluN2B subunits of the NMDAR based on their binding affinity to GluN2B and sigma-1 receptors. Here we report a comprehensive evaluation of (R)-[18F]OF-Me-NB1 and (S)-[18F]OF–Me-NB1 in nonhuman primates. Methods The radiosynthesis of (R)-[18F]OF-Me-NB1 and (S)-[18F]OF-Me-NB1 started from 18F-fluorination of the boronic ester precursor, followed by removal of the acetyl protecting group. PET scans in two rhesus monkeys were conducted on the Focus 220 scanner. Blocking studies were performed after treatment of the animals with the GluN2B antagonist Co101,244 or the sigma-1 receptor antagonist FTC-146. One-tissue compartment (1TC) model and multilinear analysis-1 (MA1) method with arterial input function were used to obtain the regional volume of distribution (VT, mL/cm3). Occupancy values by the two blockers were obtained by the Lassen plot. Regional non-displaceable binding potential (BPND) was calculated from the corresponding baseline VT and the VND derived from the occupancy plot of the Co101,244 blocking scans. Results (R)- and (S)-[18F]OF-Me-NB1 were produced in > 99% radiochemical and enantiomeric purity, with molar activity of 224.22 ± 161.69 MBq/nmol at the end of synthesis (n = 10). Metabolism was moderate, with ~ 30% parent compound remaining for (R)-[18F]OF-Me-NB1 and 20% for (S)-[18F]OF-Me-NB1 at 30 min postinjection. Plasma free fraction was 1–2%. In brain regions, both (R)- and (S)-[18F]OF-Me-NB1 displayed fast uptake with slower clearance for the (R)- than (S)-enantiomer. For (R)-[18F]OF-Me-NB1, both the 1TC model and MA1 method gave reliable estimates of regional VT values, with MA1 VT (mL/cm3) values ranging from 8.9 in the cerebellum to 12.8 in the cingulate cortex. Blocking with 0.25 mg/kg of Co101,244 greatly reduced the uptake of (R)-[18F]OF-Me-NB1 across all brain regions, resulting in occupancy of 77% and VND of 6.36, while 0.027 mg/kg of FTC-146 reduced specific binding by 30%. Regional BPND, as a measure of specific binding signals, ranged from 0.40 in the cerebellum to 1.01 in the cingulate cortex. Conclusions In rhesus monkeys, (R)-[18F]OF-Me-NB1 exhibited fast kinetics and heterogeneous uptake across brain regions, while the (S)-enantiomer displayed a narrower dynamic range of uptake across regions. A Blocking study with a GluN2B antagonist indicated binding specificity. The value of BPND was > 0.5 in most brain regions, suggesting good in vivo specific binding signals. Taken together, results from the current study demonstrated the potential of (R)-[18F]OF-Me-NB1 as a useful radiotracer for imaging the GluN2B receptors.